Name
Title | ||
|---|---|---|
TyG-er: An ensemble Regression Forest approach for identification of clinical factors related to insulin resistance condition using Electronic Health Records. |
Abstract | ||
|---|---|---|
•TyG-er identifies clinical factors correlated to insulin resistance.•TyG-er is based on Regression Forest with data imputation strategies.•TyG-er relies on 80 non-glycemic predictors from Electronic Health Records.•TyG-er indicates uricemia, leukocytes, γGT and protein profile as novel predictors. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.compbiomed.2019.103358 | Computers in Biology and Medicine |
Keywords | Field | DocType |
Insulin resistance,Pre-diabetes,Pattern recognition,Random forest,Laboratory screening,Missing values | Correlation coefficient,Pattern recognition,Predictive power,Regression,Computer science,Type 2 diabetes,Robustness (computer science),Medical record,Artificial intelligence,Imputation (statistics),Insulin resistance,Statistics | Journal |
Volume | ISSN | Citations |
112 | 0010-4825 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michele Bernardini | 1 | 2 | 3.07 |
| Micaela Morettini | 2 | 18 | 12.48 |
| luca romeo | 3 | 21 | 9.59 |
| Emanuele Frontoni | 4 | 248 | 47.04 |
| laura burattini | 5 | 21 | 14.72 |